{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## SVM（支持向量机）模型"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "3f6b7952db7d9959"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-05-08T06:09:16.806119Z",
     "start_time": "2025-05-08T06:09:16.466090Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pickle\n",
    "import os\n",
    "import time\n",
    "from sklearn import svm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.decomposition import PCA\n",
    "data_dir = r'D:\\Machine_learning\\jiqixuexi\\cifar-10-python\\cifar-10-batches-py'"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 加载数据及数据预处理"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "488acafb7e70ec7a"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# 加载CIFAR-10数据集\n",
    "def load_cifar10_data(data_dir):\n",
    "    # 加载训练数据\n",
    "    train_data = []\n",
    "    train_labels = []\n",
    "    for i in range(1, 6):\n",
    "        batch_path = os.path.join(data_dir, f'data_batch_{i}')\n",
    "        with open(batch_path, 'rb') as file:\n",
    "            batch = pickle.load(file, encoding='latin1')\n",
    "            train_data.append(batch['data'])\n",
    "            train_labels.extend(batch['labels'])\n",
    "    train_data = np.array(train_data).reshape(50000, 3, 32, 32).transpose(0, 2, 3, 1)\n",
    "    train_labels = np.array(train_labels)\n",
    "\n",
    "    # 加载测试数据\n",
    "    test_batch_path = os.path.join(data_dir, 'test_batch')\n",
    "    with open(test_batch_path, 'rb') as file:\n",
    "        test_batch = pickle.load(file, encoding='latin1')\n",
    "    test_data = test_batch['data'].reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1)\n",
    "    test_labels = np.array(test_batch['labels'])\n",
    "\n",
    "    return (train_data, train_labels), (test_data, test_labels)\n",
    "\n",
    "# 调用函数加载数据\n",
    "(train_data, train_labels), (test_data, test_labels) = load_cifar10_data(data_dir)\n",
    "\n",
    "# 数据预处理\n",
    "train_data = train_data.astype('float32') / 255.0\n",
    "test_data = test_data.astype('float32') / 255.0\n",
    "\n",
    "# 将图像数据展平为一维向量（32x32x3=3072）\n",
    "train_data = train_data.reshape(-1, 32*32*3)\n",
    "test_data = test_data.reshape(-1, 32*32*3)\n",
    "# 选取部分样本进行训练和测试\n",
    "num_train_samples = 5000  # 选取5000个训练样本\n",
    "num_test_samples = 1000    # 选取1000个测试样本\n",
    "\n",
    "train_data_subset = train_data[:num_train_samples]\n",
    "train_labels_subset = train_labels[:num_train_samples]\n",
    "test_data_subset = test_data[:num_test_samples]\n",
    "test_labels_subset = test_labels[:num_test_samples]\n",
    "\n",
    "# 使用PCA降维\n",
    "pca = PCA(n_components=0.95)  # 保留95%的方差\n",
    "train_data = pca.fit_transform(train_data)\n",
    "test_data = pca.transform(test_data)\n",
    "\n",
    "# 标准化数据\n",
    "scaler = StandardScaler()\n",
    "train_data = scaler.fit_transform(train_data)\n",
    "test_data = scaler.transform(test_data)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-05-08T06:24:44.352301Z",
     "start_time": "2025-05-08T06:09:33.948889Z"
    }
   },
   "id": "b0b3616508e91f79",
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "source": [
    "### SVM模型训练与评估"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "f0a2fbf25801b6c4"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python data analysis\\anaconda\\Lib\\site-packages\\sklearn\\svm\\_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练时间: 821.21秒\n",
      "测试时间: 131.09秒\n",
      "Accuracy: 0.5552\n",
      "Precision: 0.5557990148559036\n",
      "Recall: 0.5552\n",
      "F1-score: 0.5545580959054079\n",
      "Confusion Matrix:\n",
      " [[596  34  52  27  22  17  15  25 132  80]\n",
      " [ 31 680   7  25  11  14  16  17  35 164]\n",
      " [ 66  27 420  89 108  78 104  52  23  33]\n",
      " [ 17  36  96 434  61 153  96  35  15  57]\n",
      " [ 36  13 136  83 460  77  90  73  18  14]\n",
      " [ 15  22  83 198  66 457  53  60  17  29]\n",
      " [  7  22  77  95  72  44 619  23  11  30]\n",
      " [ 17  22  52  68  71  88  17 584  18  63]\n",
      " [110  64  15  27  15  19  12   7 667  64]\n",
      " [ 51 156   6  23   8  17  27  27  50 635]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.63      0.60      0.61      1000\n",
      "           1       0.63      0.68      0.66      1000\n",
      "           2       0.44      0.42      0.43      1000\n",
      "           3       0.41      0.43      0.42      1000\n",
      "           4       0.51      0.46      0.49      1000\n",
      "           5       0.47      0.46      0.47      1000\n",
      "           6       0.59      0.62      0.60      1000\n",
      "           7       0.65      0.58      0.61      1000\n",
      "           8       0.68      0.67      0.67      1000\n",
      "           9       0.54      0.64      0.59      1000\n",
      "\n",
      "    accuracy                           0.56     10000\n",
      "   macro avg       0.56      0.56      0.55     10000\n",
      "weighted avg       0.56      0.56      0.55     10000\n"
     ]
    }
   ],
   "source": [
    "# 创建SVM模型\n",
    "clf = svm.SVC(kernel='rbf', C=1, max_iter=10000,tol=0.001)\n",
    "# 模型训练\n",
    "start_time = time.time()\n",
    "clf.fit(train_data, train_labels)\n",
    "train_time = time.time() - start_time\n",
    "# 模型预测\n",
    "start_time = time.time()\n",
    "y_pred = clf.predict(test_data)\n",
    "test_time = time.time() - start_time\n",
    "# 模型评估\n",
    "accuracy = accuracy_score(test_labels, y_pred)\n",
    "precision, recall, f1, _ = precision_recall_fscore_support(test_labels, y_pred, average='weighted')\n",
    "conf_matrix = confusion_matrix(test_labels, y_pred)\n",
    "class_report = classification_report(test_labels, y_pred)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1-score:\", f1)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "print(\"Classification Report:\\n\", class_report)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-05-08T07:18:45.560633Z",
     "start_time": "2025-05-08T07:02:53.223481Z"
    }
   },
   "id": "40fb27f74d808881",
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 使用热力图展示混淆矩阵"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "754fa85ca996dd10"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x800 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title(\"SVM Model - Confusion Matrix (CIFAR-10)\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-05-07T14:18:51.164551Z",
     "start_time": "2025-05-07T14:18:50.394284Z"
    }
   },
   "id": "774681c1062fcf48",
   "execution_count": 7
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
